Triple
T15108248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Horsemen of the Frieze |
E360843
|
entity |
| Predicate | subjectMatterType |
P450
|
FINISHED |
| Object | processional scene |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: processional scene | Statement: [Horsemen of the Frieze, subjectMatterType, processional scene]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectMatterType Context triple: [Horsemen of the Frieze, subjectMatterType, processional scene]
-
A.
subjectMatter
chosen
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
B.
subjectMatterScope
Indicates the thematic or topical domain that an action, statement, or resource pertains to or falls within.
-
C.
subjectType
Indicates the classification or category that defines what kind of entity the subject is.
-
D.
subjectOfLaw
Indicates that a law, legal document, or legal provision is about, concerns, or applies to the referenced subject.
-
E.
countryOfSubjectMatter
Indicates the country that the subject matter (e.g., a work, topic, or issue) primarily concerns or is associated with.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d85a0491ec8190830960be8fafb994 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0058af8988190977d998f85893836 |
completed | April 15, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69deb96c1d9c81909351558ed97bc5b7 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:05 a.m.